AVMV vs. SPY: Valuation, Factor Exposure, AI, and Return Scenarios
AVMV starts cheaper and less concentrated; SPY starts with stronger recent performance and far more technology exposure
The sponsor snapshots describe economically different portfolios rather than two interchangeable versions of the U.S. stock market. AVMV's August portfolio characteristics show a 16.51x P/E, while SPY's October index characteristics show 25.13x. The source dates and vendor conventions differ, so the comparison is directional rather than a same-day accounting identity.
Starting valuation is lower for AVMV, but valuation alone does not measure business quality
AVMV's retained snapshot is cheaper on both earnings and book value. That spread is one input to future returns; it does not establish that the underlying businesses deserve identical multiples. SPY's current portfolio has more exposure to high-margin, high-ROIC technology and platform businesses, while AVMV has more cyclical and capital-intensive sectors.
Portfolio valuation multiples
AVMV has a P/E of 16.51 and P/B of 2.62; SPY has a P/E of 25.13 and P/B of 5.4.
| Portfolio observation | AVMV | SPY |
|---|---|---|
| Holdings | 299 | 504 |
| Weighted-average market cap | $28.4B | $1.71T |
| Expense ratio | 0.200% | 0.0945% |
| Top-10 weight | 12.9% | 39% |
| 2026 YTD NAV return through Sep. 30 | 8.1% | 12.7% |
The sector mix makes the comparison an economic-regime question, not only a valuation question
SPY's information-technology weight was 39.27% on October 12. AVMV's June fact sheet showed 7.22% in information technology, with much larger weights in financials, industrials, and energy. The dates differ because the study preserves the latest reviewed official snapshots rather than blending third-party estimates into a synthetic same-day portfolio.
Sector weights: AVMV and SPY
AVMV has larger financials, industrials, and energy weights; SPY has a much larger information-technology weight.
Long-run factor evidence is relevant context, but it is not AVMV's track record
AVMV launched in November 2023. A multi-decade claim therefore requires proxy evidence rather than a backfilled AVMV history. Dimensional's research-index series show that value and high profitability have beaten their comparison groups more consistently as the rolling window lengthens; the small-cap result is less persistent. These are historical frequencies, not a forecast that AVMV will reproduce them.
How often historical factor proxies outperformed
At 10-year horizons, small cap beat large cap in 68% of rolling periods, value beat growth in 78%, and high profitability beat low profitability in 90%.
AI exposure has at least four transmission channels
A technology-sector weight captures only direct ownership of many AI suppliers. The broader comparison also depends on who supplies data-center and power infrastructure, who consumes increasingly capable AI tools, and whether automation eventually raises productivity in physical businesses. The evidence below establishes the scale of adoption and infrastructure demand; it does not assign those benefits to one ETF in advance.
From direct AI rents to economy-wide diffusion
AI can affect ETF holdings through direct model, chip, and cloud rents; data-center and power investment; economy-wide software productivity; and longer-dated physical automation.
- Direct AI rentsModels, chips, cloud, platformsSPY has much greater direct exposure through its large technology and communication-services weights.
- Physical infrastructure945 TWh projected global data-center electricity use in 2030The IEA base case says data centers account for nearly half of U.S. electricity-demand growth through 2030.
- Economy-wide adoption88% organizational AI adoption in 2025Stanford also reports $285.88B of U.S. private AI investment in 2025.
- Physical automationLonger-dated productivity channelIf AI lowers the cost of perception, planning, and control, industrial and logistics users may capture productivity benefits without owning frontier models.
A separate Grizzly Bulls study measures how aggressively five large AI/cloud infrastructure spenders are reinvesting operating cash flow. That evidence is useful when considering whether direct AI beneficiaries remain unusually asset-light. Read AI Infrastructure Capital Intensity →
The return model is designed to change its answer when the assumptions change
The model combines per-share EPS growth, valuation change, a common 1.1%distribution-yield assumption, and each ETF's expense ratio. It does not add starting earnings yield separately, because doing so would double-count corporate earnings, and it does not add buybacks separately when EPS growth is already measured per share.
Five-year modeled annualized returns
SPY leads in the concentrated-AI scenario; AVMV leads in the gradual-diffusion and broad-catch-up five-year scenarios.
Ten-year modeled annualized returns
SPY leads in the concentrated-AI 10-year scenario; AVMV leads in the gradual-diffusion and broad-catch-up 10-year scenarios.
| Scenario | 5-year AVMV | 5-year SPY | 10-year AVMV | 10-year SPY |
|---|---|---|---|---|
| AI rents stay concentrated | 3.41% | 10.99% | 5.74% | 8.05% |
| Gradual AI diffusion | 8.53% | 7.60% | 7.71% | 6.10% |
| Broad earnings/value catch-up | 12.41% | 4.23% | 9.63% | 4.63% |
Ten-year sensitivity maps the growth-versus-valuation tradeoff directly
Each matrix holds the same common distribution-yield assumption and the fund's current expense ratio, then varies sustainable EPS growth and the terminal P/E. The highlighted center cell is a reference point, not a preferred forecast.
AVMV: 10-year annualized-return sensitivity
AVMV modeled 10-year returns rise as assumed EPS growth or the terminal P/E rises.
| EPS growth / terminal P/E | 15x | 16.5x | 18x |
|---|---|---|---|
| 5% EPS growth | 4.9% | 5.9% | 6.8% |
| 6.5% EPS growth | 6.4% | 7.4% | 8.3% |
| 8% EPS growth | 7.9% | 8.9% | 9.8% |
SPY: 10-year annualized-return sensitivity
SPY modeled 10-year returns rise as assumed EPS growth or the terminal P/E rises.
| EPS growth / terminal P/E | 20x | 22x | 24x |
|---|---|---|---|
| 5.5% EPS growth | 4.1% | 5.1% | 6% |
| 7% EPS growth | 5.6% | 6.6% | 7.5% |
| 8.5% EPS growth | 7.1% | 8.1% | 9% |
The matrices make the central tradeoff explicit. AVMV's lower starting multiple reduces the growth rate needed to produce a given modeled return if its terminal valuation is stable. SPY can offset its higher starting multiple if its per-share earnings growth remains sufficiently faster or if investors continue to assign it a large valuation premium.
What this study can and cannot establish
The observed data establish that the two funds begin from different valuations, concentration levels, sector mixes, and recent returns. The historical proxies establish that value and profitability effects have persisted across long samples. The scenario model establishes which combinations of growth and valuation would produce different forward return paths. None of those statements identifies the future path with certainty.
| Question | What the evidence supports | What remains unresolved |
|---|---|---|
| Starting valuation | AVMV is materially cheaper on the retained sponsor P/E and P/B observations. | How much of the discount is compensation for lower quality, cyclicality, and capital intensity. |
| Concentration | AVMV has a much lower top-10 weight in the reviewed snapshots. | Whether concentration helps or hurts future returns; concentrated winners can continue compounding. |
| Factor history | Value and high-profitability proxies have historically become more persistent over longer rolling windows. | Whether AVMV realizes those proxy premiums from today's portfolio and implementation. |
| AI | Direct AI exposure is much larger in SPY; infrastructure demand and AI adoption are also economy-wide phenomena. | How much of the eventual economic surplus accrues to suppliers versus users and physical infrastructure. |
| Absolute return | The model shows the growth and terminal-multiple combinations that change relative modeled returns. | Which scenario, if any, will occur. |
| Risk-adjusted return | AVMV's live history is too short to support a long-horizon fund-specific conclusion. | A defensible forward volatility, drawdown, and correlation model without relying heavily on proxies. |
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Research data
- Download CSVCSVAVMV vs. SPY comparison tableTabular public study data for spreadsheet analysis, independent checks, and new charts.Data snapshot October 12, 2026 · Reuse with attribution to the canonical study.
- Download JSONJSONAVMV vs. SPY structured study dataStructured public study data for programmatic verification while preserving the published field names and research context.Data snapshot October 12, 2026 · Reuse with attribution to the canonical study.
Sources and methodology
Fund characteristics come from the fund sponsors. Historical factor frequencies are external research-index evidence, not fund returns. AI and electricity-demand observations come from the Stanford AI Index and the IEA. Dates are preserved at the observation level rather than silently synchronized.
| Source | Publisher | As of |
|---|---|---|
| Avantis U.S. Mid Cap Value ETF product page | Avantis Investors | October 12, 2026 |
| Avantis U.S. Mid Cap Value ETF quarterly fact sheet | Avantis Investors | June 30, 2026 |
| State Street SPDR S&P 500 ETF Trust product page | State Street Investment Management | October 12, 2026 |
| How Often Do Small Cap, Value, and High Profitability Outperform? | Dimensional Fund Advisors | December 31, 2025 |
| Energy and AI | International Energy Agency | April 10, 2025 |
| The 2026 AI Index Report — Economy | Stanford Institute for Human-Centered Artificial Intelligence | December 31, 2025 |
Read the research standards for sourcing and versioning conventions, or contact Grizzly Bulls about a source or methodology question.